Notebook: View covid19_prediction.ipynb on nbviewer to load images.
COVID-19 spreads faster than any other disease and has an exponential growth rate around the world. Scientists aimed to flatten this curve, for which they need records and data about the impact and spread of COVID-19 in future cases for all the countries in the world. For this, we can perform predictions and Time Series Forecasting to analyze and take preventive measures to reduce the growth rate of COVID-19.
The goal of this project is to analyze the growth of COVID-19 cases and train machine learning algorithms that can forecast the future trend and predict the number of cases based on previous data.
- Input: Previous data of COVID-19 cases.
- Output: Future prediction of cases and comparison of various algorithms.
The Novel Corona Virus 2019 Dataset is available from redivis.
- Clone this repo
git clone https://github.com/kHarshit/COVID19_Forecasting.git - Download dataset from redivis.
- Install dependencies using
pip install -r requirements.txt - Execute the commands in the jupyter notebook
covid19_prediction.ipynb.